A new Lyapunov functional for stability analysis of neutral-type Hopfield neural networks with multiple delays
NEURAL NETWORKS, vol.129, pp.288-297, 2020 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 129
- Publication Date: 2020
- Doi Number: 10.1016/j.neunet.2020.06.013
- Journal Name: NEURAL NETWORKS
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Psycinfo, zbMATH
- Page Numbers: pp.288-297
- Keywords: Neutral-type Hopfield systems, Multiple delays, Lyapunov theorems, Stability analysis, DEPENDENT EXPONENTIAL STABILITY, GLOBAL LAGRANGE STABILITY, SYNCHRONIZATION CRITERIA, ROBUST STABILITY, FEEDBACK CONTROL, TIME, DISCRETE, OPTIMIZATION, SYSTEMS, STORAGE
- Istanbul University Affiliated: Yes
Abstract
This research paper conducts an investigation into the stability issue for a more general class of neutral-type Hopfield neural networks that involves multiple time delays in the states of neurons and multiple neutral delays in the time derivatives of the states of neurons. By constructing a new proper Lyapunov functional, an alternative easily verifiable algebraic criterion for global asymptotic stability of this type of Hopfield neural systems is derived. This new stability condition is entirely independent of time and neutral delays. Two instructive examples are employed to indicate that the result obtained in this paper reveals a new set of sufficient stability criteria when it is compared with the previously reported stability results. Therefore, the proposed stability result enlarges the application domain of Hopfield neural systems of neutral types. (C) 2020 Elsevier Ltd. All rights reserved.